Automated Detection of the Magnitude of Gaze Correction for Strabismus Patients

$P.Manju Bharghavi, ^Basireddy Krishnaja, #Ennawar Prajwala, ~Sangem Manisha$^#~Department of Biomedical Engineering, GRIET, Hyderabad

$manjubhargavi@griet.ac.in^basireddykrishnaja@gmail.com#ennawar.prajwala@gmail.com~manisha.sangem@gmail.com

Abstract-- Strabismus is an eye defect which, if left untreated, can result in inability to attain binocular vision or blindness in the affected eye. Early detection is critical for successful treatment. Current methods for detection involve going to an eye specialist or going to a lab with specialized equipment and lighting setup. These options can be expensive and thus aren't always available to all people. The purpose of this project was to determine the feasibility of a software-only solution that could be deployed to photo studios as a low-cost early screening mechanism. The eventual goal is to be able to detect signs of strabismus from a single high resolution portrait photograph of an infant. Several existing computer vision algorithms for detecting various eye features were investigated and the ones that would handle the requirements best for implementation were chosen. So far a good degree of detection accuracy (roughly 80%) has been achieved and currently the eye detection and corner detection routines are being fine-tuned to improve detection accuracy even more. MATLAB software is used as a tool to achieve the goal of this project. MATLAB software is one of the most powerful software using worldwide and it also consist a lot of image processing library which can be use for object detection. For this project, the program code is written using this software and it shall be efficient to detect the objects, eliminate the background image and detects the object.

Keywords—squint eye, gaze correction, strabismus, MATLAB,Hough transform, eye recognition.

1. INTRODUCTION

The medical name for Squint (or crossed-eye or lazy eye) is Strabismus which means misalignment of eyes. The incidence of squint is approximately 1.5% to 2% of population worldwide. Strabismus is an eye defect characterized by a lack of coordination between the eyes, causing the eyes to look in twodifferent directions and thus be unable to fixate on a single image. If children with strabismus are left untreated, the brain may learn to ignore the input from the weaker eye. Consequently, the children may suffer permanent loss of vision in the ignored eye. New born babies often appear cross eyed but this does not mean that they have strabismus.

The Strabismus detection algorithm consists of four phases. In the first phase, the locations of cornea (i.e. centres and diameter) of both eyes are detected for normal eyes. In the second phase, we are finding the centre and diameter for normal eyes looking in different directions (i.e. right, left, up, down directions). In the third phase, we are finding the centre and diameter for normal eyes moving horizontally with slight alignment. In the fourth phase, we are finding the centre and diameter for strabismus patients

Vladimir Vezhnevets & Anna Degtiareva[1] proposed a novel robust technique, which combines stability and accuracy. Suppose that rough eye regions are known. First, iris center and radius is detected by looking for a circle separating dark irisand bright sclera (the eye white). The iris center and radius detection is performed in image’s red- channel, which emphasizes the iris border. This is due to the fact, that iris usually exhibits low values of red (both for dark and light eyes), while the surrounding pixels (sclera and skin) have significantly higher red values. Then, upper eyelid points are found using on the observation that eye border pixels are significantly darker than surrounding skin and sclera. The detected eye boundary points are filtered to remove outliers and a polynomial curve is fitted to the remaining boundary points. In order to reduce the effect of image noise, the image is preprocessed with median filtering and 1D horizontal low-pass filtering. Finally, lower lid is estimated from the known iris and eye corners.

2 WORK

The proposed method consists of a two dimensional Hough transformation for detecting circle of unknown radius. The circular Hough transform first generates two dimensional parameter space (xc,yc) using the gradient of grayscale. The gradient is calculated from the facial image and draws a line segment to gradient direction from each edge point in the images. The radius of circle r is determined for each local maximum in the (xc; yc) space. The advantage of this approach is that the circle with unknown radius can be detected more efficiently than ordinal three dimensional Hough transform because it works in two dimensional parameter space. The eye detection step of proposed method firstly detects possible eye center by the circular Hough transform. Then it extracts histogram of gradient from rectangular window centered at each eye center. Likelihood of eye of the extracted feature vector is

evaluated and pairs of eyes satisfying predefined conditions are generated and ordered by sum of the likelihood of both eyes

Hough Transform

The Hough Transform is a method for finding shapes in an image. The Circle Hough has been implemented here, which is used to find circles within an image.

Theory: The HT can be described as a transformation of a point in a 2 dimensional region to a parameter space, dependent on the shape of the objects to be identified. The basic functionality of the HT is to detect straight lines. A straight line in the x,y-plane is described by:

y = m*x + b (1)This line is represented in the Cartesian coordinate system by its parameters b and m where m is the slope and b is the intercept. Due to the fact that perpendicular lines to the x-axis can give unbounded values for parameters m and b (b and m rises to infinity), lines are parameterized in terms of theta θ and r such that:

r= x*cos (θ) + y*sin (θ), for θ Î [0, p] (2)

Figure 1: Hough transform for r and θWhere r is the distance between the line and the origin, θ is the angle. Thus, given x and y, every line passing through point (x, y) can uniquely be represented by (θ, r). Both θ and r have finite sizes. The distance r will have the maximum value of two times the diagonal of the image.An edge detector is commonly used to provide a set of points that represents the boundaries in the image space. Equation (1) corresponds to a sinusoid curve in the (r, θ) plane unique to that point. If several points are located on the same line, they produce sinusoids crossing at the parameters for that line.

Flowchart

Phase-1

The first phase in the classification process is to take the picture of normal person eyes, looking in forward direction and finding the diameter of cornea using imdistline function, which is shown in figure 2

Figure: 2 Normal person eyes looking in forward direction with imdistline

Next applying the viscircles command to the image then the circles are drawn on the cornea. The center and radius are also shown in the figure 3

Start

Load the cropped

image

Display the image

Centres and radius are found using Hough Transform

Display the output outputcircles

Stop

Figure:3 Coordinates of the eyes looking in forward direction

Phase-2

Apply the same above procedure to the another image in which the eyes are looking towards left side. This is shown in the figure 4

Figure: 4 Coordinates of eyes looking towards left side and center, radius.

Phase-4

Pre-operative

The fourth phase in the classification process is to take the picture of a squint eye person, looking in forward direction and finding the diameter of cornea using imdistline function, this is shown in figure 5

Figure 5 Coordinates of eyes looking in forward direction and center, radius

Apply the same above procedure to the another image in which the eyes are looking in upward direction. This is shown in the figure 6

Figure 6 Coordinates of eyes looking in upward direction and center, radius.

Post-operative

Apply the same above procedure to the another image in which the eyes are looking in forward direction. This is shown in the figure 7

Figure 7 Coordinates of eyes looking in forward direction and center, radius

Apply the same above procedure to the another image in which the eyes are looking in left side direction. This is shown in the figure 8

Figure 8 Coordinates of eyes looking in upward direction and center, radius

Pre-operative

upright up Upleft

Center(left) 420.9 223.8 460.8 220.5 506.3 220.8

Center(right) 151.3 234 171.3 225.3 211.4 213.1

Radius(left) 35.5 36 32.4

Radius(right) 32.5 35.4 39.9

right centre left

Center(left) 441.6 230.8 492.7 227.5 525 238.3

Center(right) 130.7 235.1 172.2 232.6 197.4 231.8

Radius(left) 34.2 38.7 32.3

Radius(right) 38.5 35.2 35.6

Table 1 pre-operative

Post-operative

upright up Upleft

Center(left) 429 209.3 493.1 185.8 521.2 201.6

Center(right) 136.6 217.5 176.1 193.1 184.2 197.4

Radius(left) 35.5 39.1 36.9

Radius(right) 33.4 41.5 40.9

right centre left

Center(left) 401.1 222.2 471.1 219.9 524.9 214.3

Center(right) 75.3 240.9 158.8 221.7 215.1 221.9

Radius(left) 39.5 34.4 41.4

Radius(right) 39.5 33.4 38.6

Table 2 post-operative

3 CONCLUSION

This project has presented a Strabismus screening system, in which Hough Transform segmentation stage is based on accuracy and higher efficiency rate. Hough was found to be the better method for the segmentation process. So the entire process was carried out using Hough Transform. Properly detecting the inner and outer boundaries of cornea texture is important for all Strabismus screening systems. An automatic segmentation algorithm was presented, which would localize the cornea region from an eye image and isolate eyelid, eyelash and reflection areas. Threshold was also employed for isolating eyelashes and reflections of the image. Next, using Hough Transform the segmented iris region was normalized to eliminate the present dimensional inconsistencies between iris regions. The results of the work are very favourable in that they are reasonably accurate across a wide range of test cases and runacceptably fast. This gives reason for optimism about the success of the overall project. It seems that a pure software solution for the detection of strabismus is quite possible, and will merely require additional effort to produce the accuracy and consistency needed to make

4 REFERENCES

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